Dr Mohamed Elhadi Rahmani

Lecturer / Researcher

Department of Computer Science Faculty of Mathematics and Computer Science

About

Dr Mohamed Elhadi Rahmani is a lecturer and researcher in computer science at the University Dr Moulay Tahar of Saida, Algeria, and a member of the GeCoDe Laboratory (Generation and Communication of Distributed Data). He completed his entire academic career at this institution: a Bachelor's degree in Computer Science in 2012, a Master's degree in Knowledge Modeling and Reasoning in 2014, and a PhD in Computer Science in 2018, with a doctoral thesis on ecosystem computerized modeling and analysis. His research focuses on machine learning, deep learning, bio-inspired algorithms (artificial social bees, bat echolocation, black hole algorithms, fireworks optimization) and applied artificial intelligence, targeting real-world problems including plant leaf image recognition, seismic hazard prediction, climate modeling, robot execution failure detection, American Sign Language (ASL) recognition, and, most recently, federated learning combined with blockchain for secure collaborative diabetes detection. Alongside his academic activity, he is a software developer and technical blogger who runs argelhadi.wordpress.com, where he publishes programming tutorials covering Java, C#, Android, PHP/MySQL, Python, R, HTML5/CSS, JavaScript, jQuery, and XAML. He currently has an h-index of 6, an i10-index of 4, and more than 120 citations on Google Scholar.

Dr Mohamed Elhadi Rahmani

About

Research areas

Dr Mohamed Elhadi Rahmani

About

Research areas

Scientific publications

GenDiabSec: A Federated Recurrent Neural Network with Multistage DNAGen and Blockchain for Secure Collaborative Diabetes Detection

Deep Convolutional Real Time Model (DCRTM) for American Sign Language (ASL) Recognition

Multi-stage Genetic Algorithm and Deep Neural Network for Robot Execution Failure Detection

Ecological Data Exploration

Sonar Data Classification Using a New Algorithm Inspired from Black Holes Phenomenon

New Approach Based on Termite's Hill Building for Prediction of Successful Simulations in Climate Models

Bagging Approach for Medical Plants Recognition Based on Their DNA Sequences

Bio-Inspired Algorithms for Ecosystem Data Analysis

A Novel Bio Inspired Algorithm Based on Echolocation Mechanism of Bats for Seismic States Prediction

Clustering Based Sampling for Learning from Unbalanced Seismic Data Set

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

Hybridization Between Scoring Technique and Similarity Technique for Automatic Summarization by Extraction

New Supervised Approach for Plant Leaves Classification using Artificial Social Bees

A Novel Bio Inspired Algorithm Based on Echolocation Mechanism of Bats for Seismic Hazards Detection

A New Multi-layered Approach for Automatic Text Summaries Mono-Document Based on Social Spiders

Hybridization of Social Spiders and Extractions Techniques for Automatic Text Summaries

Plant Leaves Classification

Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data

A New Bio-Inspired Technique of Artificial Social Cockroaches for Spam Detection with Visual Result Mining